Total 51,464 skills
Showing 12 of 51464 skills
Analiza cambios staged en git para detectar bugs, vulnerabilidades de seguridad, malas prácticas, y genera descripciones detalladas de commits con mensaje en formato Conventional Commits. Usa este skill siempre que el usuario quiera revisar cambios antes de commitear o pushear, analizar un diff staged, detectar bugs o malas prácticas en código que está por commitear, generar un mensaje o descripción de commit, o hacer code review previo al commit. Se activa con frases como "revisá mis cambios staged", "analiza mi commit", "qué bugs tiene lo que cambié", "generame el mensaje de commit", "review antes de push", "detecta errores en mis cambios", "haceme un análisis antes de commitear", o "necesito una descripción para mi commit". NO usar para: code review de archivos sueltos sin contexto de commit, configurar linters, escribir tests, debugging de producción, o crear código nuevo. Este skill es específicamente para el momento previo al commit.
VoxFlow AI voice toolkit — text-to-speech synthesis with 200+ voices, AI podcast generation, narrated story creation, and voice search. Use this skill when users need any speech/voice/audio synthesis task.
Use when you need to write fast unit tests for Quarkus applications — including pure tests with @ExtendWith(MockitoExtension.class), @QuarkusTest with @InjectMock for full CDI mock replacement, @InjectSpy for partial CDI bean mocking, REST Assured for resource-focused tests, @ParameterizedTest with @CsvSource / @MethodSource, QuarkusTestProfile for test-specific configuration overrides, and naming conventions (*Test → Surefire, *IT → Failsafe). For framework-agnostic Java use @131-java-testing-unit-testing. Part of the skills-for-java project
Review uncommitted or recently changed files for privacy-by-design rule violations (based on privacy laws like GDPR and LGPD) before committing.
Author ZenML pipelines: @step/@pipeline decorators, type hints, multi-output steps, dynamic vs static pipelines, artifact data flow, ExternalArtifact, YAML configuration, DockerSettings for remote execution, custom materializers, metadata logging, secrets management, and custom visualizations. Use this skill whenever asked to write a ZenML pipeline, create ZenML steps, make a pipeline work on Kubernetes/Vertex/SageMaker, add Docker settings, write a materializer, create a custom visualization, handle "works locally but fails on cloud" issues, or configure pipeline YAML files. Even if the user doesn't explicitly mention "pipeline authoring", use this skill when they ask to build an ML workflow, data pipeline, or training pipeline with ZenML.
Load this skill for any up-fetch task: `up(fetch, getDefaultOptions?)`, `upfetch(url, options?)`. Covers dynamic defaults, auth, request shaping, validation, error handling, lifecycle timing, and runtime caveats.
PluginEval quality methodology — dimensions, rubrics, statistical methods, and scoring formulas. Use this skill when understanding how plugin quality is measured, when interpreting a low score on a specific dimension, when deciding how to improve a skill's triggering accuracy or orchestration fitness, when calibrating scoring thresholds for your marketplace, or when explaining quality badges to external partners like Neon.
Overview The Instagram Agent allows users to extract data from Instagram, including posts, profiles, hashtags and comments, to bypass limitations of manual research. By using the Instagram Agent, bu
A skill for reviewing a specific diff and showing the findings as comments inside difit (the diff viewer). Use it to review branch diffs, commit diffs, or GitHub PRs, then preload findings or code explanations into difit with `--comment` before launching it for the user.
Advanced Git operations wrapper. Optimizes token usage by guiding complex git workflows into efficient CLI commands.
Exploratory Data Analysis skill for CSV and parquet datasets with deterministic profiling, drift/anomaly scans, contract generation and validation, and optional memory writeback into skill-system-memory. The implementation is Polars-first (lazy scan for large files and early `--sample` head), includes high-cardinality guards for profile/importance/contract flows, and supports categorical correlation with Cramer's V. Use when building or reviewing tabular fraud/risk/data-quality workflows, profiling new datasets, checking leakage or drift, or saving/validating data contracts.
Build AI agents with Pydantic AI — tools, capabilities, structured output, streaming, testing, and multi-agent patterns. Use when the user mentions Pydantic AI, imports pydantic_ai, or asks to build an AI agent, add tools/capabilities, stream output, define agents from YAML, or test agent behavior.